Since some sentences are lost, the result may lack coherence, but important sentence extraction is one of the basic technologies for generating summaries that are useful for humans to browse.

this technique plays an important role in automatic text summarization.


However, most machine learning methods overfit the training data when many features are given.


we can expect this method to be much faster than a naïve implementation that computes tens of thousands of dot products at run time.


On the other hand, XQK took 225.28 seconds in total and its initialization phase took 174.17 seconds.

569,994 vectors were classified in 51.11 seconds.


The initialization time can be disregarded because we can reuse the above coefficents.

xQK is 224.8 (=11,488.13/51.11) times faster than TinySVM for OTHER.


The second is that some orthographic expressions with important meanings are generalized to wild cards, for instance, the numeral "H/ (how many)" may imply that the sentence is interrogative.

a method is needed to prevent some orthographic expressions from being automatically replaced with wild cards.


The generated paraphrases were evaluated by Chinese natives from two viewpoints, i.e., naturalness and meaning-retaining, with their corresponding test sentences.

7,226 generated paraphrases were correct and an 88% accuracy was achieved.


Through self-monitoring, human speakers can instantly correct their mistakes during an utterance (Levelt, 1989).

we can detect selfcorrection with local models such as the Repair Interval Model (RIM) proposed by Nakatani and Hirschberg (1993).


The RIM can detect only adjacent additions.

we i n troduce a new class { long-distance.


Moreover, this does not work if the following word is a dierent word regardless of recognition results.

we do not handle hesitations with self-corrections.


Also, the RPD necessarily depends on the verb.

the RPD and the RP of long-distance selfcorrections must be the pairs of noun phrases or adverbial phrases.


The NLP team at LIRMM currently works on thematic and lexical disambiguation text analysis (Laf01).

we built a system, with automated learning capabilities, based on conceptual vectors for meaning representation.


The question is precisely to know if they can reasonably be antonyms in the context of C. The antonymy measure M anti Eval is the angle between the sum of A and B and the sum of Anti c R (A, C) and Anti c R (B, C).

we have: The antonymy measure is a pseudo-distance.


Although STs already capture the aspect dependency induced by their varying roots, the path criterion is still not met.

we generalize the concept of a ST to that of a cohesion tree as follows: First, we observe that the construction of STs uses two types of order relations: the first, let it call ≤ 1 x , determines the order of the nodes inserted dependent on root x; the second, let it call ≤ 2 y , varies with node y to be inserted and determines its predecessor.


But whether these differences are significant, is hard to evaluate, since their theoretical distribution is unknown.

future work will be on finding these distributions.


(1) when a sourcetext sentence is replaced with a set of key-words.

this model can be regarded as a model that translates keywords into text sentences.


Interlingual machine translation and transfer-based machine translation require a parser with high precision.

these approaches to translation are not practical if we do not have enough bilingual corpora or a good parser.


Such expressions can be made up of multiple words, and there are almost infinite possible variations.

so it is quite difficult to add them and their translations to a dictionary.


The original method selects the most frequent candidate as the best one, however, this can be improved by using contextual information.

we introduce the last stage; the proposed method calculates the similarity between the original compound noun c J and its translation candidates by comparing the contexts, and chooses the most plausible translation c E .


For each word, the most frequent attribute is chosen.


The bilingual dictionary for MT system ALT-J/E, Goi-Taikei and a terminological dictionary (containing about 105,000 economic and other terms) are used to connect component words.

393 Japanese compound nouns and their translation candidates are collected and the candidates for 7 Japanese are not extracted.


It is well known that unification based grammar formalisms are Turing-equivalent in their generative capacity (Pereira and Warren, 1983;Johnson, 1988, 87-93); determining whether a given string ¡ is generated by a given grammar is equivalent to deciding whether a Turing machine halts on an empty input, which is known to be undecidable.

the recognition problem is undecidable in the general case.


According to Haas (1989), "a depth-bounded grammar cannot build an unbounded amount of tree structure from a bounded number of symbols".

for each sentential form of length there exist a finite number of partial derivation trees, guaranteeing parsing termination.


The constraint is intended to bound the depth of every derivation tree by the range of ½ times the size of its yield.


Johnson's OLP constraint is too restrictive, since it excludes all repetitive unary branching chains and -rules, furthermore, it is applicable only to skeletal grammars.

torenvliet and trautwein (1995) have suggested a more liberal constraint, which is applicable to all unification grammar formalisms.


Every application of the second rule doubles the depth of TEMP list (with respect to its length after the previous application of the rule).

the number of derivation steps for generating each È is always twice the number of steps for generating its predecessor, and for every sentential form of length Ê any partial derivation tree's depth is bounded by an exponential function of  The grammar of figure 2 is an © ¤ k grammar (viewing CAT as the category) but it does not satisfy the other constraints.


At this point the amount of pheromone assigned to each ant is weighted with respect to the quality of the solution it found.

moves involved in higher quality solutions are more likely to be chosen by ants in future iterations.


Firstly, it is not practical to attempt all possible combinations of both algorithms.

we would be required to choose a limited number of models resulting from the sk-strings technique to seed the second stage of the process.


The hybrid sk-ANT technique again  proved to be the best in terms of average deviation at 2.81%.

the newer method is preferable if only one choice is allowed.


Running an MT system on test data can be done automatically and requires no additional human language knowledge, but it requires additional time (which can be substantial for MT).

the higher performance of the P 4 pathways has some cost.


In this case, the number of elements chosen for voting is less than k. For other features, only maximum k elements among the elements of the feature projections should be chosen for voting.

we need to normalize the voting ratio for each feature.


It was TCFP without contextual information (not using formula (7)).

tCFP achieved the highest micro-average F 1 score.


Let R i (t i ) be dened as follows: R i (t i ) is equal to 1 if r i = r best ; otherwise R i (t i ) is equal to 0.

when R a (t a ) is equal to 1, the transaction t a is superior to or as good as the other translations.


The parameter k in each of the two procedures is xed so as to minimize cross-validation estimates of the deviance.

after growing each RT i (i = a; b; or c), the proposed method performs one of the simplied procedures of pruning and shrinking.


Here, P zero (c|v) and P (a i |φ c ) are computed in the detection and resolution phases, respectively (see Figure 1).

since zero pronouns are omitted obligatory cases, whether or not case c is a zero pronoun depends on the extent to which case c is obligatory for verb v. Case c is likely to be obligatory for verb v if c frequently co-occurs with v. we compute P zero (c|v) based on the co-occurrence frequency of v, c pairs, which can be extracted from unannotated corpora.


However, since estimating a semantic model, P (n i |v, c), needs large-scale annotated corpora, the data sparseness problem is crucial.

we explore the use of unannotated corpora.


However, we can regard v, c, and n i as features for a verb and its case noun because zero pronouns are omitted case nouns.

it is possible to estimate the probability based on cooccurrences of verbs and their case nouns, which can be extracted automatically from large-scale unannotated corpora.


In those applications, it is desirable that erroneous anaphoric relations are not generated.

we propose a notion of certainty to output only zero pronouns that are detected and resolved with a high certainty score.


To extract verbs and their case noun pairs from newspaper articles, we performed a morphological analysis by JUMAN and extracted dependency relations using a relatively simple rule: we assumed that each noun modifies the verb of highest proximity.

we obtained 12 million co-occurrences associated with 6,194 verb types.


At the same time, since our method requires annotated corpora, the relation between the corpus size and accuracy is crucial.

we performed two additional experiments associated with Both2.


Finally, our method integrates a parameter for zero pronoun detection in computing the certainty score.

we can improve the accuracy of our system by discarding extraneous outputs with a small certainty score.


Certainly, we also need to discharge the dependence on the surface order.

before the system confronts the actual verb valencies from the input sentence with the list of valency frames found in the lexicon, all the valency expressions are reordered.


First, in order to be able to exploit the data from PDTB, we have supplemented our grammar with the dependency specification precision on sentences percentage of 1-10 words 86.9 % of 11-20 words 78.2 % of more than 20 words 63.1 % overall precision 79.3 % number of sentences with 8.0 % mistakes in input Table 1: Precision estimate for constituents.

the output of the analysis can be presented in the form of pure dependency tree.


For example, LOC may consist of a sequence of LOCs; and ORG may include PER and/or LOC on its left.

by considering one pair of tokens at a time, and by extending the token sequence to the adjacent token one at a time, we can draw similar conclusion as that depicted in Table 1 for complex NE types.


Thus, two subsequent runs give slightly different results.


However, automatic term extraction is not the ultimate goal itself, since the large number of new terms calls for a systematic way to access and retrieve the knowledge represented through them.

the extracted terms need to be placed in an appropriate framework by discovering relations between them, and by establishing the links between the terms and different factual databases.


pattern/string matching using tag information, but also the ontological hierarchy matching to subordinate classes using either predefined or automatically derived term ontology.

semantically-based tag information retrieval can be achieved.


Table 2: TIMS -practical performance Table 2 and Figure 8 show the results: t he process ing times of TIMS were about 1.4-1.8 times faster (depending on number of tags and corpus length) than those of REM.

we assume that the TIMS tag information management scheme can be considered as an efficient mechanism to facilitate knowledge acquisition and information extraction process.


Recall that the removal of support vectors change the decision surface.

the worst happens when every support vector will become an error.


Scoring negative training documents In the TDT tracking task, the number of labelled positive training documents is small (at most 16 documents) compared to the negative training documents.

the choice of good training data is an important issue to produce optimal results.


Our window adjustment algorithm is applied each time the document discusses the target event.

some documents are assigned to more than one set of documents.


Since all of the query terms are included in the centroid, query inclusion ratio is 1 and all synonyms affect positively to the vector inner product value.

since this cluster preference is high, the ranks of all documents in this cluster changed higher.


Cluster C85 is a singleton whose centroid includes one of three query terms: bowl 0.101 marble 0.191 Since query inclusion ratio is low, the cluster preference is low.

this cluster's effect is weak to the document.


These results indicate that cluster analysis help to resolve ambiguity.

we could effectively take into account the context of all the terms in a document as well as the query terms.


SOM learns to place similar data on topologically close areas on the map.

people can choose the relevant clusters of documents on the map to get relevant documents.


Instead of using these approaches to reduce multi-dimensional vectors, we apply significance vectors to present the importance of words in each semantic category and use pre-assigned topics as axes of multi-dimensional space.

a news article can be represented by a n-dimension vector, where n is the number of pre-assigned topics.


A significance vector is presented with topic elements (t 1 t 2 …t j ), where t j presents the frequency of a word in j semantic category.

a document x is presented with: where n is the number of words and m is the number of topics.


An alternative method 2 of vector presentation can alleviate skewed distributions.

a document x is modified as: (2) Because only nouns and verbs have the hypernym relation in WordNet and because nouns and verbs convey enough information of document concepts, we remove all words except nouns and verbs found in WordNet in our experiments.


Then we average the significance of terms by dividing by the total number of terms in each gloss.

the most significance of the gloss means the most possibility of the sense.


Since there are more than 13,000 distinct Chinese characters (with around 5400 commonly-used), but only 1,300 distinct syllables, the homonym problem is quite severe in phonetic input method.

an intelligent syllable-to-word (STW) conversion for Chinese is very important.


According to the training corpus, the top 3 preferred POSs of words following " (already)" are V (verb), ADV (adverb) and ADJ (adjective).

the top 3 preferred POSs of syllable words following "yi3" should also be V, ADV and ADJ provided that " (already)" is to be identified.


To determine the class of a new word, its unit vector is compared to each class vector.

the number of calculations is reduced to the number of classes.


For this purpose the taxonomic similarity between the assigned and the correct classes is measured so that the appropriateness of a particular classification is estimated on a scale between 0 and 1, with 1 signifying assignment to the correct class.

learning Accuracy is compatible with the counting of direct hits, which, as will be shown later, may be useful for evaluating the methods.


The 7 The model has a general tendency to assign higher recall than precision values.

we have to weigh precision a little bit lower than recall (approximately in a ratio of 8:9) to see the effect.


For example, in the corpus we analyzed, Abend 'evening' and Sitzung 'session', the two components of the German compound Abendsitzung 'evening session', are much more frequent words than the latter.

a natural way to handle productively formed compounds is to treat them not as primitive units, but as the concatenation of their components.


In modifier context, nouns are sometimes followed by a linking suffix (Krott, 2001;Dressler et al., 2001), or they take other special inflectional shapes.

of the presence of linking suffixes and related patterns, the forms that nouns take in modifier position are sometimes specific to this position only, i.e., they are bound forms that do not occur as independent words.


We did not parse special modifier forms in order to reconstruct their independent nominal forms.

we treat all inflected modifier forms, including bound forms, as unanalyzed primitive nominal wordforms.


In a working word prediction system, compounds are obviously not the only type of words that the user needs to type.

the predictions provided by the compound model must be integrated with predictions of simple words.


Since, the sense vectors produced by the automatic selective sampling method are changed according to test samples and their context, we call them dynamic sense vectors in this paper (section 2.4) (Note that, the sense vectors produced in the training phase are not changed according to test samples.


Our method also shows higher performance than baseline and system A (the Naïve Bayesian method) -about 30% for baseline and about 58% for system A.

of this experiment, we proved that co-occurrence information throughout the local density and the automatic selective sampling is more suitable and discriminative in WSD.


But most of the data available now do not have it, so a large amount of labor is needed to identify the language.

the multilingual speech identification has a large part to play with unknown-language speech input.


However, transliteration is more phonetic process than orthographic process: 'h' in the Johnson does not make any Korean character (Knight et al., 1997).

patterns for E-K transliteration acquired from English/Korean alphabets as in the previous works, may not be effective.


Here, we propose ideal lenient default unifica-tion, which tries to maximize the amount of information of a result, not the amount of default information.

the problem of losing a large amount of information in structure-sharing never arises.


It is mentioned that the limitation is largely caused by inconsistencies in the corpus (Ratnaparkhi, 1996;Padró and Màrquez, 1998;van Halteren et al., 2001).

correcting the errors in a corpus and improving its quality is important.


The values of α i concentrate near the lower bound zero and the upper bound C. The examples with α i near the upper bound seem to be exceptional.

we regarded the examples with α i ≥ 0.5 as exceptional examples (i.e.


In both data sets, the proposed system produced the better performance in all classifiers.

our proposed system can be useful for all classifiers and both two different languages.


That is, according to the occurrence probability of a sequence of intentions learned from a dialogue corpus with the intention tags, the degree of similarity with each utterance is weighted based on the intentions of the last utterances.

if the utterance whose degree of similarity with the input utterance is the maximum is "sono chikaku-ni chushajo arimasu-ka?


Since the number of morphemes of S i and S e are 7 and 8, respectively, and that of morphemes in correspondence is 6, i.e., "chikaku", "ni", "no", "mise", "aru(i)" and "wa(yo)shoku".


In many cases, the intention of a user's utterance occurs in dependence on the intentions of the previous utterances of the user or those of the person to which the user is speaking.

an input utterance might also receive the influence in the contents of the speeches before it.


This violates a precondition of the 'eat' action for apples.

if both apples were equally salient, we would read eat the apple as eat the red apple.


Initial experiments with this model show considerable promise but are at too early a stage to be reported upon yet.

the graph used in most of this paper represents only nouns.


In contrast to (Davis, 1998):3:56 2 for instance, we do not assume a difference in meaning between (1) and (2).

3 in order to compute a semantics from this data without spurious ambiguity, we must be able to express the semantic generalisation that to him and him realize the same semantic argument.


Due to this, semantic predictions can be made only for NP-complements, while PPs must be treated by a separate mechanism ("content sharing account").

there is no specific prediction for the prepositional complement in English dative shift constructions.


This consideration led us to propose a different approach to measure the bias of word occurrences in a discrete way: that is, we only take words whose occurrences are saliently biased in D(T) into account, and let the number of such words be the degree of bias of word occurrences in D(T).

sAL(D(T), s), the number of words in D(T) whose saliency is over a threshold value s, is expected to be free from the background noise and sensitive to number of major subtopics in D(T).


One way to improve this situation is to eliminate the contribution of non-typical (see introduction) words.

the simplest way to archive this is to focus only on saliently occurring words (precisely, words whose occurrences are saliently biased in D(t)) and let the number of words whose saliency is over a threshold value s, denoted by SAL(D(t), s), be the degree of bias of word we need to define the saliency of occurrences of a word and a threshold value with which the occurrences of a word in D(t) is determined as salient.


In addition, we make the ∪ operator noncommutative: α ∪ β tries to match α first, and β only if it does not match α, as in Perl.

(the above grammar would mark the first Y.)


For Penn Treebank II style annotation (Marcus et al., 1993), in which a nonterminal symbol is a category together with zero or more functional tags, we adopt the following scheme: the atomic pattern a matches any label with category a or functional tag a; moreover, we define Boolean operators ∧, ∨, and ¬.

nP ∧ ¬ADV matches nP-SBJ but not nP-ADV.


The authoring mode is different from the free generation mode in that it gives the author the responsibility of choosing expansions for nonterminals rather than enumerating all possible expansions nondeterministically.

after all the obligatory expansions from dfa(D,F,A) (expansions for which there is only one possibility in the grammar) have been done, the frontier of the derivation tree contains some terminals and the nonterminals drug(D), form(F), admin(A), comments(D,A), and has to satisfy the constraint df(D,F).


However the two constraints cannot be simultaneously satisfied in KB1; This can be shown computationally by using the satisfiability check in KB1, but also by the following informal reasoning: df(D,tablet) and da(D,drink) imply that D is both in Tablet-Drugs and in DrinkDrugs; by the second fact it is in So-lutionDrugs, but SolutionDrugs and TabletDrugs have an empty intersection.

all expansions of comments lead to invalidity; hence drink is not a live choice.


The individual observations of our dataset are noun-verb pairs of three types: direct object, subject of a verb with an object, and subject of a verb without an object.

the subject and object relations of the same original sentence are considered independently by all of the models we examine.


It is preferable that a title plays both roles, because the reader may be disappointed with a gap between the title and the document if the title plays the former role poorly, and the reader may not read the document if the title plays the latter role poorly.

it is very important what title is attached to a document.


In this section, we explain the details of our questionnaire survey.

we assume that the factors which affect the reader's impression of a title which expresses newly developed technology are the following: F1 Mode of expression for titles (in other word, the expression patterns) F2 whether or not the reader has an interest in the technical field F3 The reader's level of expertise in the technical field in order to clarify what kind of impression the title based on each expression pattern (F1) gives the reader, we should investigate how the impressions that each reader who differs in F2 and F3 receives change with each expression pattern.


On the other hand, 73 dependencies, providing 5.4% of 1,362 turns consisting of more than two units, are over two utterance units.

we can conclude that utterance units are not always sufficient as parsing units of spoken language.


Automatic title generation is a complex task, which not only requires finding the title words that reflects the document content but also demands ordering the selected title words into human readable sequence.

it involves in both nature language understanding and nature language synthesis, which distinguishes title generation from other seemingly similar tasks such as key phrase extraction or automatic text summarization where the main concern of tasks is identify important information units from documents (Mani & Maybury., 1999).


On the other side, a title T with a set of common words can have a good chance to be a title for some documents even its word order is problematic.

the probability for a word sequence T to be used as a title, i.e.


Therefore, term P(T)/P({tw∈T}) is determined mainly by the word order in T and not influenced by the popularity of title words in T. As already discussed in the introduction section, the old framework for title generation allows all the words in the document equally participate in selecting title words and therefore, the final choice of title words may be influenced significantly by the common words in the document which have nothing to do with the content of the document.

we suggest a solution to this problem by introducing a hidden state called 'information source' which is able to sampled the important content words from the original document.


The hiragana and katakana syllabaries, collectively referred to as kana, are relatively small (46 characters each), and each character takes a unique and mutually exclusive reading which can easily be memorized.

they do not present a major difficulty for the learner.


the user could interpret the string ¡ as being read as atamajou or toujou and query the system accordingly.


The average relative rank was 12.8 for erroneous readings and 19.6 for correct readings.

on average, erroneous readings were ranked higher than the correct readings, in line with our prediction above.


However, we believe that certain tools widely used by the AI or NLU community are appropriate for NLG tasks.

we reformulate in more specific terms The paper describes the modules of Figure 1.2: Section 3 justifies the use of a description logic for the content determination task and its ouput, a "message"; Section 4 specifies the use of sdrt for the document structuring task and its output, a "document plan"; Section 5 exposes briefly the use of a lexicalized formalism in the tactical component.


sdrt can be viewed as a super-layer on drt whose expressiveness is enhanced by the use of discourse relations.

the drt structures (Discourse Representation Structures or drs) are handled as basic discourse units in sdrt.


For example, our system chose the following committee members to compute the centroid of the state cluster: Illinois, Michigan, Minnesota, Iowa, Wisconsin, Indiana, Nebraska and Vermont.

the centroid contains only features like those in List A.


In rare occasions, the errors can be due to misinterpretation of the guidelines, which is possible given the complexity of the syntactic constructions encountered in the corpus.

the comparison is also an opportunity of continuing the training process for the annotators.


Structurally, they are all above the verb.

they are further differentiated by secondary functional tags.


A common presumption is that word segmentation accuracy should monotonically influence subsequent retrieval performance (Palmer and Burger, 1997).

many researchers have focused on producing accurate word segmenters for Chinese text indexing (Teahan et al, 2001;Brent and Tao, 2001).


Finally, for the self-supervised learning technique, by controlling the number of EM iterations and altering the lexicon pruning strategy we obtain word segmentation accuracies of 44%, 49%, 53%, 56%, 59%, 70%, 75%, and 77%.

overall we obtain 12 different segmenters that achieve segmentation performances of 44%, 49%, 53%, 56%, 59%, 70%, 71%, 75%, 77%, 85%, 90%, and 95%.


The lower the entropy, the less uncertain the estimation of the word.

the conditional entropy is suitable as a method for the evaluation of key assignments.


If this order is neglected and the letters`ajxgukh' are assigned to a given key, the interface will become di cult for the beginners, although it might be the most e cient f o r a well-trained user.

the key assignments had better re ect such linguistic tradition.


When N K = 3, on the other hand, the number of keystrokes remains at around 6.0 per word.

when N K =3, the system requires a larger numberofkeystrokes than the baseline.


However, confined by the huge amount of manual work needed for constructing a classifier for a complicated taxonomy of questions, most question answering systems can only perform a coarse classification for no more than 20 classes.

existing approaches, as in (Singhal et al., 2000), have adopted a small set of simple answer entity types, which consisted of the classes: Person, Location, Organization, Date, Quantity, Duration, Linear Measure.


This phenomenon may be due to the fact that only a few of entities in a sentence are involved in some relations.

it is unlikely that the entity classifier can use the relation information to correct its prediction.


Although the computational time of the exact inference algorithm for loopy network is exponential, we may still be able to run it given the small number of variables that are of interest each time in our case.

we can further check if the performance suffers from the approximation.


"), the system can show the second best proposal, \Would you like one located in Kabukicho area?".

the query result can be narrowed down without forcing the user unnatural yes/no answers.


In the appliances such as VTR (Video Tape Recorder) and FAX machines, there is not a large screen to display the list of matched candidates to be selected by the user.

we address a spoken dialogue strategy to determine the most appropriate one from the list of candidates.


But it is not practical to read out all of them in order with a TTS (textto-speech) system.

dialogue is invoked to narrow down to the intended one.


Since hierarchies in the hierarchical category model are static, browsing and searching documents along the hierarchy are always done in a fix order, from the root to a leaf node.


Described in the previous section, a multidimensional category model can be transformed into flat and hierarchical category models.

there are three different classification strategies: flat-based, hierarchical-based and multi-dimensional-based methods.


However, on the other hand, the number of training data (documents) per class is reduced.

flat classification may face with the sparseness problem of training data.


Although the multidimensional category is not precisely represent any finer categories, the number of training data (documents) per class is relatively high.

multi-dimensional classification gains high accuracy for each dimension and results in high accuracy for the overall classification accuracy when there are a small number of training data.


In an n-gram model with a context tree, the length of each n-gram is increased selectively according to an estimate of the resulting improvement in predictive quality.

in general, an n-gram model with a context tree has more predictive p o wer in a smaller model.


(2001) report that an SLM with ACTs has more predictive p o wer than an SLM with a xed history reference mechanism.

if a parser based on an SLM with ACTs outperforms an SLM without ACTs, an SLM with ACTs is a promising language model as the next research milestone for spoken language understanding systems.


The basic idea of an SLM is that each w ord would be better predicted from the words that may have a dependency relationship with the wo r d t o b e p r edicted than from the proceeding (n01) words.

the probability P o f a s e n tence w = w 1 w 2 1 1 1 w n and its parse tree T is given as follows: where t i is the i-th partial parse tree sequence.


Since in a dependency grammar of Japanese, every dependency relationship is in a unique direction as shown in Figure 1 and since no two dependency relationships cross each other, the structure prediction model only has to predict the number of trees.

the second conditional probability in the right h a n d where l i is the length (number of elements) of the tree sequence t i .


An ACT is applied to a classication of the condition parts of both of the two conditional probabilities in Equation (2).

an SLM with ACTs is: where AC T w is an ACT for word prediction and AC T s is an ACT for structure prediction.


In parameter estimation and parsing, the SLM with ACTs distinguishes lexicons of function words (4 POSs) and ignores lexicons of content w ords (12 POSs) in order to avoid the data-sparseness problem.

the alphabet of the SLM with ACTs consists of 192 function words, 4 symbols for unknown function words, and 12 symbols for content words.


In our new method, the system can acquire many new high-quality translation rules from sparse translation examples based on already acquired translation rules.

acquisition of new translation rules results in the generation of more new translation rules.


Moreover, it is difficult for a developer to completely describe large-scale knowledge that can analyze various linguistic phenomena.

rule-Based MT is time-consuming and expensive.


Moreover, Example-Based MT (Sato and Nagao, 1990;Watanabe and Takeda, 1998;Brown, 2001;Carl, 2001) which relies on various knowledge resources results in the same difficulties as Rule-Based MT.

example-Based MT, which automatically acquires the translation rules from only bilingual text corpora, is very effective.


This means that the condition of acquisition of translation rules is very strict because this method allows only n:n mappings in the number of the different parts between the SL sentences and the TL sentences.

many translation rules cannot be acquired.


In all methods shown in Figure 1, the condition of acquisition of translation rules is that two similar translation examples must exist.

the systems require large amounts of translation examples.


Next, the system evaluates translation rules with variables by using the processes of combinations between the translations rules with variables and the translation rules without variables (Echizen-ya et al., 2000).

the system increases the correct translation frequencies, or the erroneous translation frequencies, of the translation rules by using these evaluation methods for the translation rules.


In our system, the 309 SL sentences became the ineffective translation results because of a lack of learning data.

the 309 SL sentences are not inadequate as evaluation data.


In this Japanese sentence, phrase " [sore wa]" results in needlessly long.

we evaluate the translation results that have different phrases to the correct translation results as the ineffective translation results in terms of the quality of translation.


On the other hand, a system with RCL can acquire many new translation rules from sparse translation examples because it uses other already acquired translation rules based on the learning algorithms described in section 2.

the quality of the translation and the effective translation rate of our system is higher than other Rule-Based MT systems.


If the programmers have to check each syntactic or semantic form by hand in order to decide if the corresponding sentence is parsed correctly, they are likely to be overwhelmed by the workload imposed by the large number of sentences, not to mention that they do not have the necessary linguistic knowledge to do this.

the toolkit should have a broadcoverage parser that has the accuracy of a parser designed specifically for a domain.


In other words, it mostly needs local dependency relationships.

we decided to build a shallow parser instead of a full parser.


2 Since this constant is not informative with respect to the relative scores of the paraphrases for a particular query, we drop it from consideration, and use only the frequencies to calculate the score of a paraphrase.

our paraphrase scoring function is (2) When calculating the score of a paraphrase using Equation 2, the following aspects regarding freq¨ § ¡ ¦ £ must be specified: (1) the extent to which the order of ¡ and £ (as it appears in the paraphrase) should be enforced; and (2) how to handle ¡ -£ pairs in the paraphrase that are absent from the lemmapair dictionary.


This may be explained by the observation that the lemma order in the queries reflects their order in the corpus.

when an ordered lemma-pair in a query matches a dictionary entry, the additional frequency count contributed by the reverse lemma order is often insufficient to affect significantly the relative score of the paraphrases.


Although this case is a transfer failure as well, the transfer has information on which parts of the input sentence failed to transfer, and such information could be a key for paraphrasing.

information on unmatched parts is also returned to the paraphraser with the result of the transfer failure.


For the actual translation we are using a pivot-based approach (the pivot is called IF for Interchange Format).

we have to develop the analysis from the textual output of an automatic speech recognition module towards the IF and the generation from the IF towards a text-to-speech input text.


The grammar code by itself represents important information in that it encodes formalizations of linguistic phenomena (in a particular linguistic framework).

users of the grammar are not only interested in the pure functionality (the input-output behaviour) of a grammar module.


That is the body of the while loop will be executed at most AE ½ times which is equal to the number of the bunsetsus except the last one.

the total number of execution of the condition part of the while loop is ¾AE ¿, which is obtained by summing up AE ¾ and AE ½.


The flow and the used data structures are very simple.


The SVM classifiers in our experiments have about forty thousand support vectors.

for every decision of dependency also a huge computation of dot products is required.


The LSD-DHMM overcomes the strong context independent assumption in traditional generative HMMs (GHMMs) and models the sequential data in a discriminative way, by assuming a novel mutual information independence.

the LSD-DHMM separately models the long state dependence in its state transition model and the observation dependence in its output model.


That is, the output model of a LSD-DHMM overcomes the strong context independent assumption in the GHMM and becomes observation context dependent.

the LSD-DHMM can also be called an observation context dependent HMM.


Compared with a translation dictionary, the advantages of a rule-based machine translation system lie in two aspects: (1) It can recognize the multi-word units, particularly separated phrases, in the source language.

our method is able to handle the multi-word alignments with higher accuracy, which will be described in our experiments.


1993) only allow word to word and multi-word to word alignments.

some multi-word units cannot be correctly aligned.


Based on and , we obtain their intersection set, union set and subtraction set.

s − = P F the subtraction set contains two different alignment links for each English word.


On the contrary, the dictionary can only list all translations.

the alignment precision of our method is higher than that of the dictionary method.


To overcome this problem (at least partially), we will smooth the lexicon probabilities of the full-form words using a probability distribution that is estimated using the word base forms.

we exploit that multiple fullform words share the same base form and have similar meanings and translations.


In Section 3, we will apply the lexicon symmetrization methods to the models described previously.

we will now sketch the standard training procedure for the lexicon model.


The linear interpolation resembles more a union of the two lexica whereas the loglinear interpolation is more similar to an intersection of both lexica.

for the linear interpolation, a word pair (f, e) obtains a large combined count, if the count in at least one direction is large.


As can be seen, removing lexical chain features results in more performance loss than removing term-overlap features.

it seems that lexical chains are more useful for the task than term-overlap.


NP$ such that (ad)nominal can be pumped to a function rooted in NP (adding the appropriate quantificational semantics), essentially making them determiners.

when building a bare NP the pumped category is necessarily the final functor, ensuring no category mismatches and the correct semantics, as shown in (16).


9 A variety of other phenomena have been implemented lexically in TCCG without the use of additional syntactic rules above and beyond the ones assumed above in 2, reducing the number of different kinds of syntactic and constructional rules common in HPSG analyses.

tCCG validates and makes more accessible the possibilities of fully lexical CCG-style analyses in HPSG without modifying the underlying framework.


Generalizations over modifier classes must be stated heterogeneously as a combination of lexical marking and pre-and posthead adjunct constructions (or alternatively stated in terms of independent linear precedence rules (Kathol, 2000)).

combining these approaches yields potential not easily realizable separately.


On the other hand, the labeled attachment score drops, but it must be remembered that these scores are not really comparable, since the number of classes in the classification problem increases from 7 to 50 as we move from the G set to the B set.

we have also included the labeled attachment score restricted to the G set for the parser using the B set (BG), and we see then that the attachment score improves, especially for Model 2.


Furthermore, any FA can be effectively turned into a deterministic FA accepting the same language.

this paper will assume that FAs are deterministic, although technically, unambiguity is sufficient for our constructions to apply.


We now characterize derivational entropy using expected rule frequencies as As already discussed, under the assumption that G p is proper and consistent we have in Gp (α) = 1 for every α.

we can write The computation of out Gp (A) was discussed in Section 3, and also H(A) can easily be calculated.


[Proof of (i)] If A G ⇒ α and α ∈ T Σ , then α = a for some a ∈ Σ 0 and the rule A → a in P has been used.


For k ≥ 2, assume that (i), (ii) and (iii) holds for any derivation of length less than k. [Proof of (i)] If A G k ⇒ α, then the rule used at the first step must be of the form A → B(C).

a Here, we have to think of the two cases: (1) yield(γ) = λ and (2) yield(γ) = λ.


Since the previous hypothesis was good, this will be a representative of a state q and thus the multiset will be a sequence of independent draws from the suffix distribution of this state P q .

l ∞ (Ŝ u,σ , P q ) < µ/4 by the goodness of the sample.


Given two random variables X and Y , a conditional probability mass function , and a marginal probability mass function , the conditional entropy of From Equation (3.8), we can see that the first three items are the values computed by the normal word trigram model as shown in Equation (2.9) and the forth item contributes to summation of the mutual information of the next word with the words in the history .

we call Equation (3.8) as a MI-Ngram model and rewrite it as: For a large enough corpus, the conditional perplexity is usually an indication of the amount of information conveyed by the model: the lower the conditional perplexity, the more information it conveys and thus a better model.


As the word trigram model is most widely used in current research, all the experiments have been done using a MI-Trigram model which is trained on the XINHUA news corpus of 29 million words(automatically segmented) while the lexicon contains about 28000 words.

the perplexities and Chinese character recognition rates of different MI-Trigram models with the same window size of 10 and different numbers of distance-dependent word pairs are shown in Table 4.


Statistical MT could generate high-quality translations if it succeeded in finding a globally optimal solution.

the models employed by statistical MT are superior indicators of the quality of machine translation.


Although the language model was effective to some degree, it could not evaluate the equivalence of the translation and the input sentence.

we concluded that the translation model is necessary for improving MT quality.


Bottom-up generation effectively uses shared nodes of the target tree, so it can improve translation speed.

bottom-up generation is suitable for tasks that require real-time processing, such as spoken dialogue translation.


Automated evaluation scores use objective parameters, such the number of N-gram matches in the evaluated text and in a gold standard reference translation.

these scores are more consistent and comparable across different evaluation experiments.


For example, for the DARPA news texts, newly generated BLEU and WNM scores confirm the observation made, on the basis of comparison of the whitepaper and the email texts, that S1 produces higher translation quality than S2, although there is no human evaluation experiment where such translations are directly compared.

the automated MT evaluation scores derive from both the "absolute" output quality of an evaluated general-purpose MT system and the complexity of the translated text.


Particularly in a dialogue, sentences tend not to have complicated nested structures, and many long sentences can be split into mutually independent portions.

if the splitting positions and the translations of the split portions are adequate, the possibility that the arrangement of the translations can provide an adequate translation of the complete input is relatively high.


shows that BTEC and SLDB are both required for handling MAD-type tasks.

in order to translate test sentences in MAD, we merged the parallel corpora, 152,170 sentence pairs of BTEC and 72,365 of SLDB, into a training corpus for HPAT and D 3 .


Unfortunately, an analysis of the parse-based heuristic on our training data (the data set will be described in Section 4), uncovered numerous, rather than just a few, sources of error.

rather than trying to handcraft a more complex collection of heuristics, we chose to adopt a supervised machine learning approach that relies on features identified in this analysis.


Many filtering expressions filter pse's that occur in their complements, but not in adjuncts.

we add variants of the previous two syntaxbased features that denote whether the parent node 5 We skip sentences where there is no decision to make (sentences with zero or one non-writer pse).


Agents are able to announce their abilities to the system at large, or indeed to the user.

when key words or phrases uttered by the user indicate that the topic of conversation has turned, for example, from accommodation booking to payment, the system's DomainSpotter (see Section 4) can ask the agents if any of them deal with payments.


Once it is handling a particular discourse segment, an agent uses its inherited confirmation strategy to compare the latest values in its current dialogue frame with the corresponding values and system intentions in the previous iteration of that frame.

the agent is able to determine which values have been confirmed (e.g.


The 'finished' agent is removed from the stack.

addressExpert will pass control back to PaymentExpert in this example, whose rules, if the user does not introduce a new topic, will continue to fire until all necessary payment information has been elicited and the payment subdialogue can be concluded -at which point control is passed back to the accommodationExpert.


However, e.g., Haliday & Hasan (1976) stress the importance of anaphoric links as a cue for coherence structures.

by Occam's Razor, we assume a single level of representation for coherence rather than multiple levels.


We introduce a theoretical framework for discriminating between Semantically Tractable (ST) questions and difficult ones, and we show that ST questions are prevalent in the well-studied ATIS data set (Price, 1990).

we show that the semantic component of the NLI task can be surprisingly easy and can be used to compensate for syntactic parsing errors.


Our first step was to hand tag a set of 150 questions with Part Of Speech (POS) tags, and re-train the parser's POS tagger.

the probabilities associated with certain tags changed dramatically.


However, this syntactic decision is inconsistent with the semantic information in PRECISE's lexicon -the preposition 'on' does not take a city and a day as arguments, rather it takes a flight and a day.

pRECISE decides to over-ride the parser and attach 'on' elsewhere.


In general, semantic over-rides can correct local attachment errors, but cannot over-come more global problems in the parse tree.

pRECISE can be forced to give up and ask the user to paraphrase her question.


Such languages typically exhibit a large number of word types in relation to word tokens in a given text, which leads to high perplexity and a large number of unseen word contexts.

probability estimates are often unreliable, even when using standard smoothing and parameter reduction techniques.


A standard statistical language model computes the probability of a word sequence W = w 1 , w 2 , ..., w T as a product of conditional probabilities of each word w i given its history, which is typically approximated by just one or two preceding words (leading to bigrams, and trigrams, respectively).

a trigram language model is described by Even with this limitation, the estimation of the required probabilities is challenging: many word contexts may be observed infrequently or not at all, leading to unreliable probability estimates under maximum likelihood estimation.


The word itself is used as another factor (W).

the above example would be factorized as follows: W:yararlanmak/R:yarar/P:NounInf-N:A3sg/ C:Nom/O:Pnon/G:NounA3sgPnonNom+Verb +Acquire+Pos Other factorizations are certainly possible; however, our primary goal is not to find the best possible encoding for our data but to demonstrate the effectiveness of the FLM approach, which is largely independent of the choice of factors.


Moreover, we verified the effect of the title revision wizard.

the titles composed using the wizard can stimulate the lay reader's interest more than the titles composed without using the wizard.


The first parsing step is a pure recogniser which computes the set of constituents to which the input words can be reduced, but not their analyses.

it is not necessary to look for further analyses once the first analysis of a constituent has been found.


Derivation of constituents by means of chain rules is much cheaper than derivation via binary rules.

the categories in line 7 are ordered such that categories from which many other categories are derivable through chain rules, come first.


In sum, no grammar format is currently available that adequately supports the encoding of a processing backbone for linearization-based HPSG grammars.

implementations of linearizationbased HPSG grammars have taken one of two options.


From the LP constraint, C cannot precede B, and from the general principle that the RHS of a rule forms a partition of its LHS, C cannot overlap B.

c cannot cover positions one, two, or three.


2  Our grammar uses the contra-dominance principle on dimensions PA and SC to express, e. g., that predicates must end up in the scope of the quantifiers whose variables they refer to.


The literals are implicitly conjoined and thus ordering does not matter.

we chose to impose an ordering on patterns, whereby the main verb appears first, followed by predicates referring to its arguments.


Since variable names play an important role in providing co-indexation between the argument of a verb and a property of that argument, designated by another predicate, terms such as 'elect(A, B, C)' and 'elect(D, E, F )' are considered to be different symbols.

a pattern like: was converted to the regular expression: The first argument of the macro/2 predicate is the name of the regular expression whereas the second argument states that the regular expression is a sequence of the symbols 'elect(A,B,C)','cperson(C)','chairman(C,D)' and so on.


Patterns containing two and three verbs were written in separate files and each entry in the files was split into two or three different segments, so that each segment contained only one verb and predicates referring to its arguments.

an expression such as: was transformed into: One can then define the automaton xpression1, consisting of the union of all first segment expressions, such as x774a, the automaton resign2, conisting of all expressions where resign is the second verb and succeed3.


Indexation places constraints on signs by requiring that co-indexed material is unifiable, on a per-dimension basis.

we do not need to overload the syntactic category with information from different dimensions.


Once we have fixed a specific element of this table, the maximization steps can be done in 1)).

the complexity of this algorithm is in setting k = 0 results in a search algorithm that is monotone at the phrase level.


Similarly, the constraint in Equation 2 avoid the pattern on the right-hand side in Figure 4.

if we enforce the constraints in Equation 1 and Equation 2, we cannot violate the ITG constraints.


that the IBM models are also a mapping from source positions to target positions.

this method is comparable to the IBM models for the source-to-target direction.


Second, the features have to be static in the sense that we have to fix the costs before aligning any word.

we cannot apply dynamic features such as the IBM-4 distortion model in a straightforward way.


between German and English, the probability of the empty word alignment is not very high.

plenty of alignment points are produced by the MWEC algorithm, resulting in a high recall and low precision.


That article also introduces the Model 6; additionally, state-of-the-art results are presented for the Verbmobil task and the Canadian Hansards task for various configurations.


To make it clear, consider the example in Table 1 again.

for the noun phrase [ 7 This mutant], the annotated preceding coreferential clusters are: three training instances are generated: Among them, the first two instances are labelled as negative while the last one is positive.


Inspired by the qualitative observations we identified a new qualitative condition for feature vector evaluation -top joint feature rank.

feature vector quality can be measured independently of the final similarity output.


The maximal recall and precision are achieved by updating the dictionary from time to time with the help of the changing target language 8 .

of analysis there is a text where every string has at least one interpretation and no legitimate interpretation is excluded.


However, f (x) is not necessarily a good confidence measure.

we use Preference Learning proposed by Herbrich et al.


Then, "with modifies saw" is preferred to "with modifies girl."


If w i is next to a very long base NP or a sequence of base NPs, w i cannot get information beyond the NPs.


If we train SVM by using the whole data at once, training will take too long.

we split the data into six groups: nouns, verbs, adjectives, prepositions, punctuations, and others.


When we removed the base NP information (b i , L i , R i ), it dropped to 94.9%.

the base NP information improves RNF's performance.


Preference Learning gave better scores than Collins' Model 3 parser for these subproblems.

we expect that our method is also applicable to phrase structure parsers.


While the pure CG only involved functional application rules for combining categories, CCG introduces several additional combinatory rules for both syntactic and semantic composition -forward and backward composition, and crossed composition, as well as substitution rules.

ccG covers a wide range of linguistic phenomena, including various kinds of coordination.


The existing RSCM of the wordê i is the sum of the ranks of MT outputs in an N-best list containing the wordê i in a position that is aligned to i in the Levenshtein alignment, which is normalized by the total rank sum: is the Kronecker function, that is, if words/morphemes x and y are the same, δ(x, y) = 1; otherwise, δ(x, y) = 0.

only in the case whereê i and L i (ê I 1 , w In 1 ) are the same, the rank of the MT output w In 1 , N − n, is summed.


To conduct a fair comparison, the number of MT outputs in the system's N-best list and the number of MT outputs in the mixture are expected to be the same.

the authors used either a threebest list from each of three MT systems or a fivebest list from each of two non-SMT MT systems for the proposed RSCM and a ten-best list for the existing RSCM.


The curves of the existing RSCM with reordering are closer to a square than those of the existing RSCM.

the performance of the proposed RSCM on the non-SMT systems, D 3 and HPAT, are much better than that of the existing RSCM.


In Figure 7, the curve of the proposed RSCM is a little closer when CRR is larger than CAR; and the curve of the existing RSCM is a little closer when CAR is larger than CRR.

the performance of the proposed RSCM on the SMT system, SAT, is a little better than that of the existing RSCM in the case where CRR is regarded as important; similarly, the performance of the proposed RSCM on the SMT system is a little worse than that of the existing RSCM in the case where CAR is regarded as important.


3 Text-level Discourse Analyzing The original search space of a discourse parser is enormous ( Marcu 2000).

a crucial problem in discourse parsing is search-space reduction.


Each textual unit completes an argument or a topic that the writer intends to convey.

a text span should have semantic links to text spans in the same textual unit before connecting with text spans in a different one.


In our experiment, the output of the previous process was used as the input of the process following it.

the accuracy of one level is affected by the accuracies of the previous levels.


Phrasal alternations are more than 4x more common, and Reorderings occur over 20x more frequently.

while string difference methods may produce relatively clean training data, this is achieved at the cost of filtering out common (and interesting) paraphrase relationships.


The main hypothesis underlying this methodology is that, given a text and its translation into another language, the semantic information is mostly preserved during the translation process.

if the texts in one language have been semantically annotated and their translations have not, annotations can be transferred from the source language to the target using word alignment as a bridge.


The Italian translation maintains the synonymy at the phrase level but the single component words do not.

if the single words were aligned any transfer from English to Italian would be incorrect.


First, in SemCor all multiwords included in WordNet are explicitly marked.

kNOWA does not need to recognize English multiwords, although it still needs to recognize the Italian ones.


In practice, given the difficulty in deciding what is a lexical unit and what is not, we decided to accept the transfer of a word sense from an English lexical unit to an Italian free combination of words (see for instance occhiali da sole annotated with the sense of sunglasses).

only the lack of synonymy at lexical level has been considered an annotation error.


It is sometimes very difficult to distinguish between annotation errors and nontransferable word senses, also because we are not English native speakers.

we preferred to be conservative in marking English annotations as errors unless in very clear cases.


As will become clear, however, our use of human validators will allow us to extend the usefulness of the corpus in a variety of ways.

we can use it to build new sorts of applications for information retrieval in the domain of consumer health.


The taxonomies reflecting popular lexicalizations in all domains are indeed much less elaborate at both the upper and lower levels than in the corresponding technical lexica.

(Medin and Atran, eds., 1999) there are no popular terms linking infectious disease and mumps, so that in the popular medical taxonomy of diseases the former immediately subsumes the latter.


(Computerized medical information systems do not offer the possibility of follow-up in the sort of cases of misunderstanding which we have in communication between laypersons and medical practitioners.)

while MWN will contain only word forms that are used by nonexperts (and thus part of natural rather than technical language), it must for practical reasons record word senses that are peculiar to the technical vocabulary.


Note that, in all of the above, MFN and MBN have characteristically played different roles.

where MFN has been associated with constructing practical tools designed to assist users in coming to believe what is true, MBN has been associated with research, for example regarding what people believe about medical phenomena.


With a broad coverage grammar, it is possible that the parser could run almost indefinitely on sentences that are difficult to parse.

we set an upper limit on the number of constituents that can be added to the chart before the parser quits.


Most current methods adopt a statistical framework, without full semantic analysis of the selected content passages; this makes the comparison of content across multiple selected text passages hard, and necessarily approximated by the textual similarity of those passages.

most current summarization or longanswer question-answering systems employ two levels of analysis: a content level, where every textual unit is scored according to the concepts or features it covers, and a textual level, when, before being added to the final output, the textual units deemed to be important are compared to each other and only those that are not too similar to other candidates are included in the final answer or summary.


Current summarization systems often represent concepts indirectly via textual features that give high scores to the textual units that contain important information and should be used in the summary and low scores to those textual units which are not likely to contain information worth to be included in the final output.

many summarization approaches use as conceptual units lexical features like tf*idf weighing of words in the input text(s), words used in the titles and section headings of the source documents (Luhn, 1959;H.P.Edmundson, 1968), or certain cue phrases like significant, important and in conclusion (Kupiec et al., 1995;Teufel and Moens, 1997).


Partial matches between textual and conceptual units introduce a new problem, however: if two textual units partially cover the same concept, it is not apparent to what extent the coverage overlaps.

there are multiple ways to revise equation 1in order to account for partial matches, depending on how conservative we are on the expected overlap.


estimate the model parameter from some data, and use the system to translate sentences which may not be well covered by the training data.

the potential of adaptation techniques needs to be explored for machine translation applications.


The 2-grams and 3-grams can be encoded using this operator too.

our final structured query model is a sum operator over a set of nodes.


Both annotators agreed that a strict left-to-right approach is highly impractical, because the intended argumentative structure of the text often becomes clear only in retrospect, after reflecting the possible contributions of the segments to the larger scheme.

they very soon settled on a bottom-up approach: First, mark the transparent cases, in which a connective undoubtedly signals a relation between two segments.


It is clear that the correspondence among the sentences is very complex.

robust and accurate alignment is essential.


In order to achieve such alignment, we need not only syntactic information but also semantic information.


In addition, it appears again with two skips, i.e., 'ab**a.'

abaca's vector has "1+ " in the component corresponding to 'aba.'


In multiple document summarization, sentences are often reorganized.

it is more effective to decompose a sentence into DTP sets and to compute similarity between the DTPs.


For ESK and WSK, we have to choose parameters, .

we tuned these parameters for a development set.


A word dictionary for a Markov modelbased system is often constructed from a training corpus, and no unknown words exist in the training corpus in such a case.

when the parameters of the above probabilities are trained from a training corpus, words that appear only once in the training corpus are regarded as unknown words and decomposed to characters with POC-tags so that statistics about unknown words are obtained 2 .


The Random Indexing approach is motivated by the Johnson-Lindenstrauss Lemma (Johnson and Lindenstrauss, 1984), which states that if we project points into a randomly selected subspace of sufficiently high dimensionality, the distances between the points are approximately preserved.

if we collect the random index vectors into a random matrix R of order c × k, whose row R i is the k-dimensional index vector for context i, we find that the following relation holds: That is, the Random Indexing context matrix G contains the same information as we get by multiplying the standard cooccurrence matrix F with the random matrix R, where RR T approximates the identity matrix.


For example, expressions such as "I don't think X is good", "I hope that X is good" are not favorable opinions about X, even though "X is good" appears on the surface.

we use top-down pattern matching on the tree structures from the full parsing in order to find each sentiment fragment, that is potentially a part of a sentiment unit.


Many techniques which have been studied for the purpose of machine translation, such as word sense disambiguation (Dagan and Itai, 1994;Yarowsky, 1995), anaphora resolution (Mitamura et al., 2002), and automatic pattern extraction from corpora (Watanabe et al., 2003), can accelerate the further enhancement of sentiment analysis, or other NLP tasks.

this work is the first step towards the integration of shallow and wide NLP, with deep NLP.


Intuitively a good evaluation metric should give higher score to a good translation than a bad one.

a good translation should be ranked higher than a bad translation based their scores.


We adopt this assumption and add one more assumption that automatic translations are usually worst than their reference translations.

reference translations should be ranked higher than machine translations on average if a good automatic evaluation metric is used.


the gunman kill police Using S1 as the reference translation, S2 has a ROUGE-L score of 3/4 = 0.75 and S3 has a ROUGE-L score of 2/4 = 0.5, with β = 1.

s2 is better than s3 according to ROUGE-L.


Using Equation 6 and f(k)-=-k 2 as the weighting function, the ROUGE-W scores for sequences Y1 and Y2 are 0.571 and 0.286 respectively.

y1 would be ranked higher than y2 using WLCS.


In order to compute BLEU at sentence level, we apply the following smoothing technique: Add one count to the n-gram hit and total ngram count for n > 1.

for candidate translations with less than n words, they can still get a positive smoothed BLEU score from shorter n-gram matches; however if nothing matches then they will get zero scores.


In a small portion of the corpus (6.7%), all coreference links were annotated.

the size of the annotated data (3,115 personal pronouns 1 , 2,198 possessive pronouns, 928 demonstrative pronouns) compares favourably with the size of evaluation data in other proposals (619 German pronouns in (Strube and Hahn, 1999), 2,477 English pronouns in (Ge et al., 1998), about 5,400 English coreferential expressions in (Ng and Cardie, 2002)).


For every combination of feature values the learner has the freedom to choose different factors and to assign different strength to them.

the main problem is not choice and interaction of factors, but rather the formulation of anaphora resolution as a classification problem.


In this case, we find that the most recent antecedent always contains little information to directly determine the coreference relationship with the anaphor.

for a given anaphor, the corresponding special constraint agent can always safely filter out these less informative antecedent candidates.


As mentioned above, the corpus for a spelling recognition task is unfortunately not available at this time.

this work applies the NECTEC-ATR Thai Speech Corpus, constructed by NECTEC (National Electronics and Computer Technology Center) incorporated with ATR Spoken Language Translation Laboratories.


A feature vector used in our experiment is a 39-feature vector, consists of 12 PLP coefficients and the 0 th coefficient, as well as their first and second order derivatives.


When we consider how summaries are produced, it seems more natural to identify important segments in the document set and then produce summaries by combining and rephrasing such information than to select important sentences and revise them as summaries.

we believe that second type of extract is superior and thus we prepared the extracts in that way.


However, the cost of this evaluation is huge.

we employ a pseudo question-answering evaluation, i.e., whether a summary has an 'answer' to the question or not.


However, it is natural for the group to engage in multiple tasks.

we use the term "task shift" to refer to adjacent segments of the thread (comprised of emails) which reflect distinct group goals.


In contrast, instead of extracting email texts verbatim, we extract single sentences from particular emails in the thread.


In this matrix, rows represent sentences and columns represent unique words found in the thread.

the cells of a row store the term frequencies of words in a particular sentence.


Our approach was designed to operate on the new textual contributions of each participant.

the emails underwent a limited preprocessing stage.


We use cross-validation to choose Markov-order and perform feature selection.

each training set is randomly split-80% used for training and the remaining 20% for validation-and based on validation set performance, choices are made for model structure, prior, and which word lexicons to include.


When a word w tagged T 1 in a reference corpus has been wrongly tagged T 2 by the tagger, that means that T 1 and T 2 are lexically and syntactically ambiguous, with respect to w and a given context.

t 1,2 is a potential candidate for an ambiguous tag.


The time to first relevant document should not be the only criterion in order to judge the navigation effectiveness.

some interfaces can require longer getting to the first relevant document, but after that it can fully benefit from additional features.


Consequently, by providing separate frequencies of occurrence for individual variants instead of a single frequency of occurrence calculated for a term candidate unifying all variants, the corpus-based measures and termhoods are distributed across different variants.

we aim at enhancing the statistical evaluation of termhoods through conflation of different surface representations of a given term, and through joint frequencies of occurrence of all equivalent surface forms that correspond to a single concept.


Our aim is to form synterms prior to the syntactic estimation of termhoods for term candidates.

after the extraction of individual term candidates, we subsequently normalise them in order to generate synterms, where the normalisation is performed according to the typology of variations described in Section 2.


are all acronyms for retinoic acid receptor alpha).

in addition to extracting acronyms, we further gather all acronym variants and their EFs, and we map them into a single CR.


This approach ensures that all term variants are naturally dealt with jointly, thus supporting the fact that they denote the same concept.

we expect that precision would be enhanced by considering joint frequencies of occurrence and termhoods for all variants of candidate terms, while recall would benefit by the introduction of new candidates through consideration of different variation types.


If there is a loop in the merged graph, the degree of a node on the loop is at least 2.

all of the nodes on the loop are aligned nodes.


The paths A and B map r to the same target language node.

the target language tree fragments for A and B are connected.


On the other hand, when the translation is known, we can efficiently determine a better alignment.


There are (l + 1) m candidate alignments for a target length l and O (m) candidate target lengths.

naiveOp-timalDecode explores Θ (m(l + 1) m ) alignments.


, m} as follows: ψ(i) is the set of source positions which are mapped to the target location i by the alignmentã and the fertility of the target position i We can rewrite each of the IBM models P r (f ,ã|e) as follows: Table 2 shows the breaking of P r (f ,ã|e) into the constituents T i , D i and N i .

we can write P r (f ,ã|e) P r (e) as: where L i = trigram(e i |e i−2 , e i−1 ) and λ is the trigram probability of the boundary word.


Note that each word e i has only a constant number of candidates in the vocabulary.


To resolve this problem, we propose a summarization method that produces a concise and condensed term description from multiple paragraphs.

a user can obtain sufficient information about a term with a minimal cost.


These descriptions are obtained by the organization module in Figure 2.

the related term extraction module is independent of our summarization method.


Because a set of input documents (in our case, the paragraphs for a single term) were written by different authors and/or different time, the redundancy and divergence of the topics in the input are greater than that for single document summarization.

the recognition of similarity and difference among multiple contents is crucial.


However, due to the rudimentary nature of existing methods, we use hand-crafted rules to complement simple sentences with the subject.

we can obtain the following two simple sentences from the above-mentioned input sentence, in which the complement subject is in parentheses.


However, the pattern-based method fails to classify the sentences that do not match with any predefined patterns.

second we classify the remaining sentences into the group in which the most similar sentence has already been classified.


We found that the top 38 paragraphs, on average, contained all viewpoint types in the top 50 paragraphs.

the remaining 12 paragraphs did not provide additional information.


It should be noted that although the VBS method targets 12 viewpoints, the sentences selected from the miscellaneous group can be related to the remaining 16 viewpoints.

even if we focus on the 28 viewpoints, the coverage of the VBS method can potentially increase.


For that purpose, most past work (Appelt, 1985;Dale and Haddock, 1991;Dale, 1992; Dale and Reiter, 1995;Heeman and Hirst, 1995;Horacek, 1997;Krahmer and Theune, 2002;van Deemter, 2002;Krahmer et al., 2003) makes use of attributes of an intended object (the target) and binary relations between the target and others (distractors) to distinguish the target from distractors.

these methods cannot generate proper referring expressions in situations where no significant surface difference exists between the target and distractors, and no binary relation is useful to distinguish the target.


Observing the collected expressions, we found that starting from a group with all of the objects, subjects generally narrow down the group to a singleton group that has the target object.

a referring expression can be formalized as a sequence of groups (SOG) reflecting the subject's narrowing down process.


The algorithm presented in the previous section outputs several possible expressions.

we have to choose one of the expressions by calculating their scores.


Inter-dependencies among ordering decisions within different constituents (e.g., for achieving parallelism) make the global sentence ordering problem challenging and are certainly worth investigating in future work.

for the present, we constrain the possible model types considered here by assuming that the ordering of any constituent is independent of the ordering within other constituents in the tree, including its daughters; consequently, ( ) ( ) Given this independence assumption, the only possible ordered trees are trees built with nonterminal constituents computed as follows: for each ( ) In fact, we can further constrain our search for the best ordering of each unordered constituent B, since C's head must match B's head: we have reduced the problem to finding the best ordering of each constituent of the unordered tree.


Unlike (Larkey and Croft, 1996) we do not merge our classifiers by linear combination, because the RegEx module does not return a scoring consistent with the vector space system.

the combination does not use the RegEx's edit distance, and instead it uses the list returned by the vector space module as a reference list (RL), while the list returned by the regular expression module is used as boosting list (BL), which serves to improve the ranking of terms listed in RL.


These methods are ineffective for computing purpose, especially when a large number of the features are involved and the interaction among them is unclear.

a machine learning approach is applied and the empirical studies are carried out in our work.


Obviously, it is not a simple task to map the combined effects of the thirteen linguistic features to the corresponding relations.

a machine learning approach is proposed, which investigates how these features contribute to the task and how they should be combined.


This finding suggests that temporal indicators should be divided into two groups according to their semantic roles rather than grammatical categories.

we propose the third model, namely Semantic Role based Grouping Model (SRG), in which the indicators are manually re-marked as TI_j_pos or TI_at_pos 4 .


For example, in English, someone may mistype "from" as "form," and string information will tell us nothing because both words are common.

we use contextual information to detect this kind of mistyping.


Katakana cannot express the precise pronunciation of loanwords, because the katakana transliteration of a loanword is an attempt to approximate the pronunciation of its etymon (the foreign word from which it is derived).

katakana orthography is often irregular, thus the same word may be written in multiple ways.


For example, both Japanese words "6 7 0 (punch)" and "6 7 4 (panty)" are transliterated into "panti" by our romanization system.


The ordinary edit distance between two strings is defined as the number of edit operations (insertion, deletion, and substitution, although sometimes substitution is not permitted) required to edit from one string into the other.

the ordinary edit distance is a positive integer value.


Orthographic variants appear less frequently than their standard expressions, and we cannot expect to have much contextual information for orthographic variants.

we always have to cope with this problem even when we process very large corpus, because the appearance of variants does not relate to the size of the corpus.


For C2E, Table 8 shows that even with only 9 percent training set, our approach can still make 20 percent absolute word error rate reduction.

although the experiment are done in different environments, to some extend, Table 7 and Table 8 reveal that the n-gram TM/DOM outperforms other techniques for the case of English/Chinese transliteration/back-transliteration significantly.


We formed the equation as follows: Then the number of all hypotheses about verb vj is recorded as n, and the number of those for scfi as m. According to Bernoulli theory, the probability P that an event with probability p exactly happens m times out of n such trials is: And the probability that the event happens m or more times is: In turn, P(m+, n, p e ) is the probability that scfi wrongly occurs m or more times with a verb that doesn't match it.

a threshold of 0.05 on this probability will yield a 95% confidence that a high enough proportion of hypotheses for scfi have been observed for the verb legitimately to be assigned scfi (Korhonen 2001).


Fortunately, common sense has it that translation preserves meaning 12 , and, by definition, a multilingual corpus, like the one we use, contains corresponding utterances in different languages.

we shall assume that if two sentences A 1 and A 2 in two different languages are translations of one another (noted A 1 ↔ A 2 ), then, they should be the linguistic realisations of the same meaning, and reciprocally 13 .


In (Hastie 98), the authors proposed to minimize the Kullback-Leibler (KL) distance between the r ij and u ij where r ij and u ij are the probabilities of a pairwise a i and a j in the estimated model and in our model, respectively, and n ij is the number of training data in which their classes are a i or a j .

to find the minimizer of equation 6, they first calculate letting ∆l(p) = 0, they proposed to find a point satisfying where i = 1, 2, ...k and p i > 0.


CR D , and compare them using the semantic similarity measures.

we map the nouns contained in the utterances to their respective WordNet senses and operate on these representations in the subsequent steps.


As the table shows, Gold Standard 1 includes only 3.69% of all markables.

we used Gold Standard 2 in the evaluation reported in Section 5.


A large corpus is required to make this method fully powerful.

we computed TF*IDF scores for every word on the basis of 2431 Switchboard dialogues (ca.


As has already been noted by Yarowsky (1994), using lemmas helps to produce more concise and generic evidence than inflected forms.

building classifiers based on lemmas increases the data available to each classifier.


Here are some examples of sentences from a PC call center's logs, along with the extracted dependency pairs: customer broke a tp customer...break, break...tp end user broke a ThinkPad end user...break, break...ThinkPad In these examples, "customer" and "end user" and "tp" and "ThinkPad" can be assumed to have the same meaning in terms of this analysis for the call center's operations.

these two sentences have the same meaning, but the differences in expressions prevent us from recognizing their iden-tity.


After POS tagging for each sequence of words in a document, it is possible to apply a cascaded set of rules, successively identifying more and more complex phrasal groups.

simple patterns will be identified as simple noun groups and verb groups, and these can be composed into a variety of complex NP configurations.


We can see the satiation point at 20 in the figure.

we set N equal to 20 in our experiments for synonymous expression extraction from the entire corpus.


Whenever several Spanish substitution alternatives for a Portuguese subword had to be considered (cognate ambiguity) that particular one was chosen which had the closest relative distribution in the corpus-derived Spanish word frequency list, when compared to its Portuguese equivalent in the Portuguese word list.

we obtained a list of tentative Spanish subwords each linked by the associated MIDs to its corresponding cognate in the Portuguese lexicon.


c) It is currently impossible to look beyond the sentence boundary.

the reference of a agent cannot be detected if it occurs in the preceeding sentence.


Based on the misses and the noise, we adapted the style module as well as the error annotation schema.

in a first annotation schema we assigned more than 30 different error codes literally taken from (Ges, 1999) and (Uni, 2000).


Transliterated words are mainly proper nouns and technical terms, and such words are often not registered.

a back transliteration framework for creating new words would be very useful.


The correspondence of an English letter or string to a katakana character or string varies depending on the surrounding characters, i.e., on its English context.

our back transliteration method uses the target English context to calculate the probability of English letters corresponding to a katakana character or string.


But, since MWVs has various POS tag patterns, it is not practical to assign each pattern an according syntactic rule.

a variation of finite state automaton is considered in our approach for the extraction of MWVs.


In our experiment, we suppose that it is neither the verb head nor the preposition that determines the reliability of such MWV structures.

we only focus on the distribution of the rest words (able or important) in the corpus.


are often the most frequent verbs both in specific and general English language.

the syntactic and semantic combinations of such verbs and other words are quite rich, which led to a relative low score of E(c) in our experiment.


It should be noted that the MUC template structure does not link the field fillers onto particular instances in the texts.

if the same entity description is used more than once then there is no simple way of identifying which instance corresponds to the event description.


WHISK was evaluated on the same texts from the MUC-6 data but the nested template structure proved too complex for the system to learn.

soderland produced his own simpler structure to represent events which he described as "case frames".


The minimum possible set of components which can form an event are (1) PersonIn, (2) PersonOut or (3) both Post and Org.

a sentence must contain a certain amount of information to be listed as an event in this data set: the name of an organisation and post participating in a management succession event or the name of a person changing position and the direction of that change.


These are treated as separate events in the MUC format.

they are split into the separate post names and an event generated for each in the common representation.


The structural error causes the errors in the dependency parse tree including having "end" as a root of the entire tree and the wrong dependency from "announced" to "the major department" in Figure 5 2 .

the accumulation of the errors resulted in missing the organization name "Okajima".


Furthermore, replies do not happen immediately, so that responders need to take special precautions to identify relevant elements of the discourse context (for example, by quoting previous messages).

email is a distinct linguistic genre that poses its own challenges to summarization.


A problem with using statistical sentenceshortening for summarization is that syntactic form does not always correlate with the importance of the information contained within.

syntactic sentence shortening might get rid of important information that should be included in the summary.


To this end, we use the DL classifier because it bases its classifications solely on the most reliable piece of evidence identified by the criteria.

dL classifier enables us to learn which is the partof-speech and the space distribution of this indicator by using the criterion [1gr|mform|ordered|all].


On the contrary, indicators for verbs tend to be mainly situated after the verb.

a non-symmetrical context shifted forward by a word proves to be more appropriate.


The n-gram that is situated next to the word to disambiguate can thus be assimilated to the (n+1)-gram which contains it.

the question becomes: do n-grams have to contain the word to disambiguate or at least to be adjacent to it?


The lemmatisation variability of this determiner, this preposition or this apostrophe is low for a given common noun located at a given distance from a given target word.

the piece of information brought out by the juxtaposition of the noun and the preceding word is often very similar to the piece of information provided by the noun alone.


In this case, the hypernym of the sense is not the last word but A.

the hypernym of 0efb60 is not the last word " " (go; word) but " " (kaisuu; number) in Figure 3.


However, we assume that this model can not be trained for low frequency words due to a lack of training data.


In the experiment in Section 5, SVM classifiers were trained for words which occur more than 20 times in the training corpus.

the simple ensemble described here is summarized as follows: we use the SVM classifier for high frequency words those which occur more than 20 times and the Naive Bayes classifier for the low frequency words.


In other words, the Naive Bayes classifier proposed here could probably handle many more low frequency words than the baseline classifier.

it was more effective to combine the Naive Bayes classifier with the SVM classifier rather than the baseline classifier in order to improve the robustness of the overall WSD system.


The notion of word sense that has been generally adopted in the literature is an artifact of several factors in the status quo, notably the availability of lexical resources such as machinereadable dictionaries, in which fine sense distinctions are not supported by criteria for selecting one sense rather than another, and WordNet, where synset groupings are taken as defining word sense distinctions.

for instance, Senseval-2 WSD tasks required disambiguation using WordNet senses (see, e.g., discussion in Palmer et al.


The example Chinese sentences are not translations of the English sentences.

the set of example sentences within a frame is enriched, providing better coverage for MT and CLIR systems.


On the other hand, HEAIH can acquire a hypernym not included in N if the hypernym contains substrings included in N .

we also compared the performance under the assumption that only the hypernyms included in N could be true hypernyms.


The feature vector x associated with a collocation candidate represents the cooccurrence frequency information for this candidate: where O ij are the cell counts of a 2 × 2 contingency table (Evert, 2004).

we have a fourdimensional feature space Ω ⊆ R 4 , and each association measure defines a SF g : Ω → R. The selection of collocation candidates is usually made in the form of an n-best list, but may also be based on a pre-defined threshold γ.


Statistical significance tests are designed to account for a small fraction of this variation that is due to random effects, assuming that all parameters that may have a systematic influence on the evaluation results are kept constant.

they provide a lower limit for the variation that has to be expected in an actual repetition of the experiment.


However, one has to keep in mind that γg(n), which is determined from the candidate set C, is itself a random variable.

a is not a fixed acceptance region and its variation counter-balances that of Na.


should be equal to the average precision π A .

p (T A | N A ) should follow a binomial distribution with success probability π A , i.e.


While this region contains exactly N A = n candidates in the current evaluation, N A may be different from n when the experiment is repeated.

π A is not necessarily identical to the average precision of n-best lists.


However, this conclusion is premature because the two rankings are not independent.

the observed precision values of g 1 and g 2 will tend to vary in the same direction, the degree of correlation being determined by the amount of overlap between the two rankings.


Given acceptance regions A := A g 1 (γ 1 ) and B := A g 2 (γ 2 ), both SF make the same decision for any candidates in the intersection A ∩ B (both SF accept) and in the "complement" Ω \ (A ∪ B) (both SF reject).

the performance of g 1 and g 2 can only differ in the regions D 1 := A \ B (g 1 accepts, but g 2 rejects) and B \ A (vice versa).


The number of candidates in the difference regions, N D 1 and N D 2 , may be small, especially for acceptance regions with large overlap (this was one of the reasons for using conditional inference rather than a normal approximation in Section 3.1).

it is advisable to use Fisher's exact test (Agresti, 1990, 60-66) instead of an asymptotic test that relies on large-sample approximations.


Previous studies into the use of intensifying adverbs have shown an overuse of the token very.

it is a likely candidate to be considered as a marker of non-native language use.


These, we expected, were likely to be rather abundant, while they would have been subjected to a relatively low degree of editing.

they would constitute likely candidates for filtering.


The parts of a collocation cannot be substituted by semantically similar words.

'gut' in 'to spill gut' cannot be substituted by 'intestine' (see also Lin (1999)).


While these association measures have their statistical merits in collocation identification, it is interesting to note that they have relatively little to do with the linguistic properties (such as those mentioned at the beginning) which are typically associated with the notion of collocativity.

it may be interesting to investigate whether there is a way to implement a measure which directly incorporates linguistic criteria in the collocation identification task, and even more important, whether such a linguistically rooted approach would fare better in comparison to some of the standard lexical association measures.


The probability 2 of a particular supplement 3 5 4 7 6 8 6 @ 9 , is described by its frequency scaled by the sum of all supplement frequencies: (1) of a PNV triple can be described by its most probable supplement: To define a measure of collocativity w for a candidate set, some factor regarding frequency has to be taken into account.

besides v x w y , we take the relative co-occurrence frequency for a specific PNV triple and incorporate it as a second factor to h w i Standard procedures for evaluating the goodness of collocativity measures usually involve identifying the true positives among the -highest ranked candidates returned by a particular measure.


Although the modifiability property constitutes common wisdom about collocations, it has not yet been empirically evaluated.

we ran an experiment which took both the PNV triples classified as collocations and the PNV triples classified as non-collocations and counted the numbers of distinct supplements (referred to as in Subsection 3.3).


Moreover, the collocational proportion curve already ends with 54 distinct supplements, whereas the non-collocational proportion curve leads up 520 distinct supplements.

we are able to add some empirical grounding to the widespread textbook assumption about the limited modifiablity of collocations.


All it requires is some sort of shallow syntactic analysis, e.g., POS tagging and phrase chunking.

in the future we plan to include other syntactic types of collocations, such as verb-object or verbobject-PP combinations, and also apply our methodology to other languages and domains, such as the biomedical field.


The SO of a given word is calculated from the strength of its association with a set of positive words, minus the strength of its association with a set of negative words.

the SO of a word, word, is calculated by SO-PMI as follows: where Pwords is a set of 7 positive paradigm words (good, nice, excellent, positive, fortunate, correct, and superior) and Nwords is a set of 7 negative paradigm words (bad, nasty, poor, negative, unfortunate, wrong, and inferior).


For example, ' ' (luck) has inherently a positive meaning, but it can construct both positive word ' ' (lucky) or a negative word ' ' (unlucky).

it is not easy to define the paradigm set simply by intuition.


The 'noise' may be 'creative use' or even incorrect use.

the number of ready-to-label word types formed from a particular morpheme was limited to 50, but it must cover 80% of the tokens of all word types carrying the morpheme in the corpus (i.e., 80% dominance).


The algorithm was further run with just 14, 10 and 6 morphemes, giving a precision of 79.15%, 79.89% and 75.65%, and a recall of 79.50%, 73.26% and 66.29% respectively.

conveniently defined morphemes in Chinese enhance the effectiveness of the algorithm by simplifying processing and yielding better results even in a smaller corpus compared with what Turney (2003) used.


Although one can obviously think of counter-examples, we would generally expect that the more specific term dog can only be used in contexts where animal can be used and that the more general term animal might be used in all of the contexts where dog is used and possibly others.

we might expect that distributional generality is correlated with semantic generality -a word has high recall/low precision retrieval of its hyponyms' co-occurrences and high precision/low recall retrieval of its hypernyms' co-occurrences.


Thus, we might expect that distributional generality is correlated with semantic generality -a word has high recall/low precision retrieval of its hyponyms' co-occurrences and high precision/low recall retrieval of its hypernyms' co-occurrences.

if n 1 and n 2 are related and P(n 2 , n 1 ) > R(n 2 , n 1 ), we might expect that n 2 is a hyponym of n 1 and vice versa.


For example, "strong tea" is a type of "tea" and "to  Table 5: Correlation with compositionality for different similarity measures rip up" is a way of "ripping".

we hypothesised that a distributional measure which tends to select more general terms as neighbours of the phrasal verb (e.g.


Finally, we obtained a very similar result (0.217) by ranking phrasals according to their inverse relative frequency with their simplex constituent (i.e., f req (simplex) f req(phrasal) ).

it would seem that the three-way connection between distributional generality, hyponymy and relative frequency exists for verbs as well as nouns.


Any pair of word represents a parameter in distance models.

these models have to deal with combinatorial explosion problems, especially when longer sequences are considered.


We try to reduce the bias of the estimator by using a large corpus.

we want to scan the whole corpus efficiently in order to make this framework usable.


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